Generation of high-intensity ultra-short optical pulses: 2018 Nobel Prize Winners in Physics Gerard Mourou and Donna Strickland
Bibliographic record
Abstract
In the early 1980s, French physicist G. Mourou and his Canadian collaborator D. Strickland solved the problem of power drop by dispersing in time and space the processes of amplification and compression: a method of obtaining super-powerful chirped laser pulses (CPA – chirped pulse amplification). The paper presents brief biographical references to Mourou and Strickland. The 2018 Nobel Prize in Physics was awarded “for groundbreaking inventions in the field of laser physics”: Artur Isidorovich Ashkin (Ashkinazi, born 02.09.1922, USA), half of the prize “for the optical tweezers and their application to biological systems”; Gerard Albert Mourou and Donna Theo Strickland (became the third woman to be awarded the Nobel Prize in Physics) (quarterly premium) “for their method of generating high-intensity, ultra-short optical pulses”. Since that time all lasers have been built on a new principle: after the amplifiers place a compressor from diffraction bars. Instead of simply amplifying the pulse, it is first spread out on spectral components spread over time, then they are amplified separately, then again assembled into a single pulse. At each point in time, only a fraction of the pulse is amplified, not the entire pulse, allowing for a much higher peak intensity of laser light flow.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".